{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "#api_key_project = \"\" #MK's Key\n",
    "api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 6  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '250'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_research_assistants'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.11"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_121647-gaebztyz</code>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gaebztyz' target=\"_blank\">finetune_chetGPT_hatespeech_research_assistants</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gaebztyz' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gaebztyz</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gaebztyz?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x1f37e5c77c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_6__task_1_train_set_250 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_6__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_6__task_1_train_set_250 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_6__task_1_train_set_250\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 879.8299492385787, 869.0\n",
      "p5 / p95: 853.0, 921.4\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.233502538071066, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346653 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1733265 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 997\n",
      "mean / median: 880.86, 869.0\n",
      "p5 / p95: 853.0, 928.2\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.98, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~220215 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1101075 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 566868\n",
      "\n",
      "#### Estimated cost: 4.53 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-cJxLBJx7Z6YMJy6c07zPnrzR\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich:ds-6-t-1-trn-250:B5t7ENmj has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 48/63: training loss=0.04, validation loss=0.02\n",
      "Step 49/63: training loss=0.08, validation loss=0.05\n",
      "Step 50/63: training loss=0.07, validation loss=0.17\n",
      "Step 51/63: training loss=0.06, validation loss=0.13\n",
      "Step 52/63: training loss=0.08, validation loss=0.13, full validation loss=0.07\n",
      "Step 53/63: training loss=0.05, validation loss=0.06\n",
      "Step 54/63: training loss=0.02, validation loss=0.05\n",
      "Step 55/63: training loss=0.03, validation loss=0.05\n",
      "Step 56/63: training loss=0.04, validation loss=0.02\n",
      "Step 57/63: training loss=0.04, validation loss=0.05\n",
      "Step 58/63: training loss=0.04, validation loss=0.06\n",
      "Step 59/63: training loss=0.03, validation loss=0.03\n",
      "Step 60/63: training loss=0.02, validation loss=0.13\n",
      "Step 61/63: training loss=0.04, validation loss=0.12\n",
      "Step 62/63: training loss=0.01, validation loss=0.03\n",
      "Step 63/63: training loss=0.03, validation loss=0.04, full validation loss=0.05\n",
      "Checkpoint created at step 39\n",
      "Checkpoint created at step 52\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [03:17<00:00,  2.00it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.8260869565217392,\n",
      "                 'precision': 0.8142857142857143,\n",
      "                 'recall': 0.8382352941176471,\n",
      "                 'support': 136},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.9155555555555556,\n",
      "                       'precision': 0.9279279279279279,\n",
      "                       'recall': 0.9035087719298246,\n",
      "                       'support': 228},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.7741935483870969,\n",
      "                  'precision': 0.75,\n",
      "                  'recall': 0.8,\n",
      "                  'support': 30},\n",
      " 'accuracy': 0.8730964467005076,\n",
      " 'macro avg': {'f1-score': 0.8386120201547973,\n",
      "               'precision': 0.8307378807378808,\n",
      "               'recall': 0.8472480220158239,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.8739093888457768,\n",
      "                  'precision': 0.8751533622091997,\n",
      "                  'recall': 0.8730964467005076,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_6_t_1_trn_250>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_6__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_6__task_1_train_set_250'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [05:31<00:00,  1.49it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.7168674698795181,\n",
      "                 'precision': 0.6329787234042553,\n",
      "                 'recall': 0.8263888888888888,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.8454011741682974,\n",
      "                       'precision': 0.8470588235294118,\n",
      "                       'recall': 0.84375,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.5655172413793104,\n",
      "                  'precision': 0.803921568627451,\n",
      "                  'recall': 0.43617021276595747,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.7611336032388664,\n",
      " 'macro avg': {'f1-score': 0.7092619618090419,\n",
      "               'precision': 0.7613197051870394,\n",
      "               'recall': 0.7021030338849488,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.7546765929947163,\n",
      "                  'precision': 0.7764466041391145,\n",
      "                  'recall': 0.7611336032388664,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_6_t_1_trn_250>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>█▁</td></tr><tr><td>f1</td><td>█▁</td></tr><tr><td>precision</td><td>█▁</td></tr><tr><td>recall</td><td>█▁</td></tr><tr><td>train_loss</td><td>▄█▇▆█▅▂▃▄▄▄▃▂▄▁▃</td></tr><tr><td>train_mean_token_accuracy</td><td>▃▂▄▃▁▂▄▄▄▆▆▆▆▄██</td></tr><tr><td>valid_loss</td><td>▁▂█▆▆▃▂▂▁▃▃▂▆▅▂▂</td></tr><tr><td>valid_mean_token_accuracy</td><td>█▇▁▃▃▆▇▆█▇▆█▅▄▇▇</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.76113</td></tr><tr><td>f1</td><td>0.76113</td></tr><tr><td>precision</td><td>0.76113</td></tr><tr><td>recall</td><td>0.76113</td></tr><tr><td>train_loss</td><td>0.02528</td></tr><tr><td>train_mean_token_accuracy</td><td>1.0</td></tr><tr><td>valid_loss</td><td>0.04198</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.9854</td></tr></table><br/></div></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">finetune_chetGPT_hatespeech_research_assistants</strong> at: <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gaebztyz' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/gaebztyz</a><br/>Synced 6 W&B file(s), 4 media file(s), 4 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>.\\wandb\\run-20250228_121647-gaebztyz\\logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mark the end of the run\n",
    "wandb.finish()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chatGPT",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
